Self-aware image segmentation methods and systems

ABSTRACT

The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35U.S.C. §371 of International Application No. PCT/IB2016/052425, filed on Apr.29, 2016, which claims the benefit of U.S. Provisional PatentApplication No. 62/163,101, filed on May 18, 2015. These applicationsare hereby incorporated by reference herein.

BACKGROUND

The following relates generally to the image processing arts, imagesegmentation arts, and related arts, and to applications employingsegmented images such as urology treatment planning, inverse planningfor intensity-modulated radiation therapy (IMRT), and so forth.

In various imaging tasks such as urology treatment planning, radiationtherapy planning, and so forth, the prostate or other organ or tumor ofinterest is segmented in a computed tomography (CT), magnetic resonance(MR), ultrasound (US), or other 2D or 3D medical image. The segmentationprocess entails delineating boundaries of the prostate, tumor, or otheranatomical feature(s) of interest in the image. Various approaches maybe used to perform the segmentation, such as an adaptive mesh fittingapproach or a region growing approach. Most automated segmentationapproaches are iterative in nature.

A problem in such automated segmentation approaches is that sometimesthe segmentation algorithm fails to converge to the correct solution,e.g. the mesh may be erroneously fitted to something other than theorgan of interest, or the growing region may leak out of a gap in theregion boundary. Conventionally, the solution is to have a radiologistor other trained professional review the segmentation result foraccuracy, and, if an inaccurate result is obtained, the radiologisttakes suitable remedial action.

Segmentation failures are reduced, but have not been eliminated, bytraining the segmentation algorithm on a large set of training images.The training set should encompass the range of image variations likelyto be encountered, but complete coverage of all possible variants isgenerally not possible. Moreover, robustness of the segmentation processdepends on the initial conditions (e.g. initial mesh, or seed locationsfor region growth approaches).

The present disclosure provides approaches for addressing this problemand others.

SUMMARY

In one aspect, an apparatus for segmenting a medical image includes atleast one processor programmed to: perform multiple, parallelsegmentation processes on an input image to generate a plurality ofsegmentation results; mark each segmentation result of the multiple,parallel segmentation processes as a potential success (PS) or potentialfailure (PF); and combine the segmentation results marked as PS toproduce an output segmentation result for the input image.

In the apparatus as described in the preceding paragraph, the pluralityof segmentation results may include both intermediate segmentationresults and a final segmentation result for each segmentation process ofthe multiple, parallel segmentation processes; and optionally, only thefinal segmentation results marked as PS are combined to produce theoutput segmentation result for the input image. The at least oneprocessor may be further programmed to declare an overall failure if apercentage of the multiple, parallel segmentation processes having anintermediate segmentation result marked as PF reaches a predeterminedthreshold. The multiple, parallel segmentation processes may beiterative segmentation processes; the plurality of segmentation resultsmay include both intermediate segmentation results produced by nonterminal iterations of the segmentation processes and a finalsegmentation result produced by each segmentation process; and the atleast one processor may be further programmed to: at each iteration ofthe iterative segmentation processes, adjust a measurement criteria usedin marking each segmentation result of the multiple, parallelsegmentation processes as a PS or PF. The marking operation may include:identifying a largest group of mutually similar segmentation results,wherein: segmentation results belonging to the largest group of mutuallysimilar segmentation results may be marked as PS; and segmentationresults not belonging to the largest group of mutually similarsegmentation results may be marked as PF. The multiple, parallelsegmentation processes may employ different segmentation processinitializations. The different segmentation process initializations maybe generated by random perturbations of a baseline segmentation processinitialization. The at least one processor may be further programmed togenerate an uncertainty or confidence interval for the outputsegmentation result based on a statistical variation of the segmentationresults marked as PS. Each segmentation result may be marked with aprobability value PPS of being a PS and with a probability value PPF ofbeing a PF, where for each segmentation result PPS may be in a range[0,1], PPF may be in a range [0,1], and PPS+PPF=1. The at least oneprocessor may be further programmed to, prior to the performingmultiple, parallel segmentation processes on the input image: preprocessthe input image; and classify, with a binary classifier, the input imageas a segmentable or not segmentable. The preprocessing may includeperforming at least one of the following on the input image: smoothing;contrast enhancement; edge detection; or non-rigid deformation. The atleast one processor may be further programmed to perform a trainingphase in which the binary classifier is trained by receiving multipletraining images wherein each training image of the multiple trainingimages may be labeled as segmentable or not segmentable.

In another aspect, an image segmentation method includes: classifying,with a computer implemented binary classifier, an input image assegmentable using a computer implemented segmentation process or notsegmentable using the computer implemented segmentation process;segmenting the input image using the computer implemented segmentationprocess if the input image is classified as segmentable; and performinga remedial process if the input image is classified as not segmentable.

The method as described in the preceding paragraph may includeperforming computer implemented pre-processing of the input image priorto the classifying, the classifying being performed on the pre-processedinput image; wherein the remedial process may include performing furthercomputer implemented pre-processing of the input image. The method mayfurther include: acquiring the input image using a medical imagingsystem; wherein the remedial process may include acquiring a new inputimage using the medical imaging system with a different imagingconfiguration. The method may further include that during a trainingphase performed prior to the classifying, training the binary classifierusing a computer implemented training process operating on a set oftraining images each labeled as segmentable or not segmentable. Themethod may further include segmenting each training image using thecomputer implemented segmentation process and labeling the trainingimage as segmentable or not segmentable based on an output of thesegmenting. The method may further include that the computer implementedsegmentation process comprises multiple, parallel segmentationprocesses. The method may further include that each segmentation processof the multiple, parallel segmentation processes is different from everyother segmentation process of the multiple, parallel segmentationprocesses. The method may further include that each segmentation processof the multiple, parallel segmentation processes has a differentsegmentation process initialization generated by a random perturbationof a baseline segmentation process initialization. The method mayfurther include that the computer-implemented segmentation processfurther comprises (1) grouping segmentation results of the multiple,parallel segmentation processes to identify a group of mutually similarsegmentation results and (2) generating a final segmentation result forthe input image based on the group of mutually similar segmentationresults.

One advantage resides in providing a robust system of determining if afailure has occurred in an image segmentation process.

Another advantage resides in minimizing supervision needed to ensureproper segmentation.

Another advantage resides in providing a self-aware image segmentationprocess.

Other advantages will become apparent to one of ordinary skill in theart upon reading and understanding this disclosure. It is to beunderstood that a specific embodiment may attain, none, one, two, more,or all of these advantages.

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 diagrammatically shows an image segmentation system.

FIG. 2 diagrammatically shows leveraging of the multiple, parallelsegmentation processes performed by the system of FIG. 1 to produce arobust output segmentation result.

FIG. 3 diagrammatically shows an alternate embodiment of an imagesegmentation process.

FIG. 4 shows an illustrative example of an ultrasound prostate imagesegmentation.

DETAILED DESCRIPTION

In segmentation approaches disclosed herein, statistical techniques areapplied to provide “self-aware” segmentation which is capable ofautomatically assessing the quality or reliability of the outputsegmentation result. In one disclosed approach for self-awaresegmentation, multiple, parallel segmentation processes are performedwith different segmentation process initiations, for example produced byrandom perturbations of a baseline segmentation process initiation. Theresults of these multiple, parallel segmentation processes are grouped(e.g. clustered) to identify a largest group of mutually similarsegmentation results, which are then combined using a voting process,(weighted) averaging, or some other aggregation technique. The approachis “self-aware” in that the clustering identifies the mutually similar(and hence presumably “correct”) segmentation results, while discardingthe outlier (and hence presumably “wrong”) segmentation results. On theother hand, if no sufficiently large and/or sufficiently mutuallysimilar group of segmentation results can be identified, then overallsegmentation failure is thereby recognized automatically. A further“self-aware” aspect is that a variance, standard deviation, or otherstatistical variation of the “correct” segmentation results provides aquantitative uncertainty or confidence interval for the outputsegmentation result.

In another disclosed approach, a segmentation process (which may in someembodiments comprise multiple, parallel segmentation processes as justdescribed) is applied to a set of training images, and each trainingimage is labeled as either (1) segmentable if the segmentation result isdeemed satisfactory or (2) not segmentable if the segmentation result isdeemed unsatisfactory. These labels may be applied manually (e.g. byhaving a radiologist or other skilled medical professional evaluate thesegmentation result) or using some automated process. These trainingimages are then used to train a binary classifier to classify an inputimage as either segmentable or not segmentable. In an inference phase,the trained binary classifier is applied to an input image to determinewhether it is segmentable or not segmentable. If the input image isclassified as segmentable, then the segmentation process is applied witha high likelihood of success due to the input image having successfullypassed the classifier. If the input image is classified as notsegmentable, then some remedial process is applied. For example, if theclassifier is applied during the imaging session then the remedialprocess may be to acquire a new input image using a different imagingconfiguration. As another example, if the input image is pre-processedbefore being classified then further (possibly different) pre-processingmay be applied. This approach is “self-aware” in that the trained binaryclassifier provides automated awareness as to whether the input image issegmentable using the segmentation process.

With reference to FIG. 1, an input image 2 is optionally preprocessed inoperation 4. The input image 2 may be an image from, for example,computed tomography (CT), magnetic resonance (MR), ultrasound (US), orother medical imaging source, and may be a two-dimensional (2D) image(or image slice) or a three-dimensional (3D) image (or image volume).The preprocessing is done, for example, to prepare an image for latersegmentation. The preprocessing may include contrast enhancement, edgedetection, non-rigid deformation to align with a reference image orstructural model, fusing of various thusly pre-processed images, or soforth.

After the input image 2 is preprocessed, the image is sent to an imagequality (IQ) binary classifier 6. Binary classifier 6 determines if theimage is segmentable, that is, may be successfully segmented, by aparticular computer-implemented segmentation process. Said another way,the classifier 6 classifies the image as either segmentable or notsegmentable. The classifier is trained, as described below, so that ifthe image is classified as segmentable then there is a high likelihoodthat the computer-implemented segmentation process will be able tosuccessfully segment the image; whereas, if the image is classified asnot segmentable, then there is a high likelihood that thecomputer-implemented segmentation process will fail to segment theimage.

If the image is classified as not segmentable by the classifier 6, thenin the illustrative embodiment a segmentation failure is reported 8, andoptionally some further remedial action is taken such as performingfurther preprocessing 4 (which may be further iterations of the samepreprocessing that was initially performed, and/or some different typeof preprocessing), or segmenting the image manually or semi-manually inan operation 10 (for example, by a radiologist operating a graphicaluser interface to draw contour lines around features). More generally,the response to the image being classified by the classifier 6 as notsegmentable is to perform some remedial action. As another example, ifthe classifer 6 is applied during the imaging session (possibly withoutperforming the pre-processing 4), then the remedial action may includeacquiring a new input image using the medical imaging system (e.g CT,MR, US, or so forth) with a different imaging configuration. On theother hand, if the image is classified as segmentable by the classifier6, then the image segmentation process is performed on the input (andoptionally preprocessed) image with a high likelihood of success due tothe image having successfully passed the classifier 6.

To provide accurate prediction of whether an input image is segmentableby a given image segmentation process, the binary classifier 6 istrained in a training phase performed for that segmentation process. Inone approach, training images with various (possibly different orperturbed) preprocessing are segmented using the chosen segmentationalgorithm, with each result being labeled (e.g. manually) as successfulor failed so as to create a labeled training set of input images. A setof image features (including, e.g., image histogram, gradient histogram,histogram moments or so forth) is extracted from each training image,and binary classifier 6 is then trained on the feature vectors tooptimally distinguish images that can be successfully segmented usingthe segmentation algorithm from images for which the segmentationalgorithm fails.

In a variant embodiment, binary classifier 6 may be trained onas-acquired (not pre-processed) images which thereafter pass through afixed preprocessing/segmentation pipeline and are labeled as to whetherthey were successfully segmented. In this case, the trained classifiermay be applied to as-acquired images during the imaging session tovalidate whether the images can be successfully segmented using thechosen preprocessing/segmentation pipeline if not, then remedial actioncan be immediately taken in the form of acquiring further images usingdifferent image acquisition settings until images that can besuccessfully segmented are obtained.

With continuing reference to FIG. 1 and with further reference now toFIG. 2, if binary classifier 6 determines that an image may besuccessfully segmented, then the chosen computer-implementedsegmentation process is applied with a high likelihood of success. Inthe illustrative example of FIGS. 1 and 2, the chosencomputer-implemented segmentation process comprises multiple, parallelsegmentation processes 12 which are performed concurrently on the image(after the optional preprocessing 4). Advantageously, each segmentationprocess 12 may be different than every other segmentation process 12. Asused herein, the term “computer-implemented” segmentation processdenotes a segmentation process that is executed automatically (andtypically, though not necessarily, iteratively) by a computer, withoutrelying upon input received from a user such as user-drawn contoursexcept possibly as part of the initialization of thecomputer-implemented segmentation process (for example, user-drawncontours could delineate an initial mesh that is thereafterautomatically fitted to image structure of interest by thecomputer-implemented segmentation process).

Running multiple parallel segmentation processes optionally leveragesparallel computing resources such as multi core computers, computingclusters, supercomputers, graphical processing units (GPUs) or the liketo perform the set of parallel segmentation processes 12 concurrently,each starting from different initial conditions (e.g. differentperturbed initial meshes, or different seed points). It is expected thatmost of these segmentation processes will converge to the (same) correctsolution, and hence be similar to each other. On the other hand,segmentation failures will output (different) wrong solutions. Astatistical analysis approach (e.g. agreement checking operation 14) isused to identify the largest group of mutually similar (correct) resultsand discard the smaller group(s) of dissimilar (wrong) results. This maybe done, for example, by computing pairwise similarities betweensegmentation results and performing k-means clustering or anotherclustering algorithm. With the groups identified, correct results (thosebelonging to the largest group of mutually similar segmentation results)are marked as potential successes (PS), and wrong results (those notbelonging to the largest group of mutually similar segmentation results)are marked as potential failures (PF). If no sufficiently large group ofmutually similar results is obtained, then overall segmentation failureis reported in operation 18.

If a sufficiently large group of similar results is obtained, then theseare aggregated or combined by averaging or voting or the like togenerate the final (correct) segmentation result, as shown in operation16. Statistical variations amongst the group of similar (correct)results (that is, the segementation results marked as PS) may optionallybe used to provide an uncertainty or confidence interval for the outputsegmentation result.

To provide useful information in a statistical sense, the multiple,parallel segmentation processes 12 should employ different segmentationprocesses and/or different segmentation process initializations. Forexample, in one approach, different segmentation process initializationsare generated by random perturbations of a baseline segmentation processinitialization (e.g., different random perturbations of an initialmesh).

In the illustrative example, each segmentation result is marked aseither PS or PF. This is an exclusive, i.e. hard allocation of thesegmentation results. In variant embodiments, a soft allocation may beemployed—for example, each segmentation result may be marked with aprobability value P_(PS) of being a PS and with a probability valueP_(PF) of being a PF, where for each segmentation result P_(PS) is in arange [0,1], P_(PF) is in a range [0,1], and P_(PS)+P_(PF)=1. Theprobabilities P_(PS) and P_(PF) may, for example, be assigned based ondistance in the feature vector space from the centroid of the largestcluster of mutually similar segmentation results. In soft allocationembodiments, P_(PF) may be thresholded to provide a count ofsegmentation results whose probability of failure is above the thresholdfor the purpose of identifying an overall segmentation failure 18.

As particularly illustrated in FIG. 2, the multiple, parallelsegmentation processes 12 are, in some embodiments, each an iterativesegmentation process. In this case, the agreement checking 14 may beapplied after each iteration, or after some number N iterations (or,alternatively, after some execution time period over which some of thesegmentation processes 12 may have run more iterations than others). Thefailure report 18 issues if the fraction of the parallel segmentationprocesses 12 marked as PF at any checked iteration exceeds the overallfailure threshold.

Discarding wrong results provides increased robustness for the overallclustering, which may optionally be leveraged to allow use of a faster,simpler (but perhaps less reliable) segmentation process for theindividual segmentation processes 12 that are run concurrently, so as toat least partially offset the increased computational cost of performingmultiple parallel segmentation processes. Discarding wrong resultsprovides a technical advantage because, for example, it reduces: (i) theprocessing burden on any processors and (ii) the storage space requiredin any memory. As previously noted, the disclosed approach of performingmultiple, parallel segmentation processes also efficiently utilizesparallel computing resources if available. Thus, the disclosedsegmentation approaches improve performance of the computer itself.

With reference to FIG. 3, in one variant embodiment, an output from theagreement checking unit 14 is sent back to the binary classifier 6.Advantageously, this allows for reclassification of an image aftersegmentation processes have been run on the image. The reclassificationin turn allows for the possibility of additional preprocessing to betterprepare the image for subsequent segmentation processes 12.

FIG. 4 illustrates an example of ultrasound prostate image segmentation.This example uses a statistical shape model, and the model iscontinuously updated to adapt to local shape changes as the object shapevaries in the video. This method works well when the prostate boundaryeach video frame is correctly segmented and the new shapes can be usedfor online improving the statistical shape model. However, once a set offrames is not correctly segmented, the error may be compounded oraccumulated and propagated through the rest of the segmentation and afailure results. The workflow of this example is as follows.

In the example of FIG. 4, global population-based shape statistics(GPSS) is first computed by using a number of manually segmentedcontours obtained from a number of different subjects' transrectalultrasound (TRUS) video sequences in row/operation 40. The GPSSdeformable contour is used to segment the first N frames from frame 0 toframe N−1 independently. The mean shape in the GPSS is used toautomatically initialize the segmentation contour. The resulting shapeswill be stored. After that, an initial adaptive local shape statistics(ALSS) is computed by using the segmented contours from those N framesin row/operation 42. This ALSS is then used as the shape constraint ofthe deformable contour for segmenting the next frame as shown inrow/operation 44.

As the example continues and as shown in the row/operation 42 of FIG. 4,the deformable contour using ALSS will go back to segment the firstframe of the video sequence, which is now considered as the N+1th frame,with the previous segmentation result as the initialization. After thesegmentation is done, the obtained prostate shape will be added into thetraining shape set. ALSS is learned by using the new set of trainingshapes. With the updated ALSS, the deformable contour moves to segmentthe next frame. The learning and segmentation process is repeated untilthe whole video sequence is segmented.

The capability for self-aware failure detection may be added at twostages. The first stage may be that multiple, parallel segmentationprocesses 12 are applied to the segmentation from one frame to the nextframe. Instead of using a single initialization for next framesegmentation, a set of perturbed initializations can be generated. Thus,multiple segmentation processes are created and each process has adifferent initialized shape. If the segmentation goes well, the resultsfrom different processes tend to agree. Otherwise, significantlydifferent segmentation results may be obtained; this will lead to thesuspicion of segmentation failure as indicated by failure report 18 ofFIG. 1. The second stage at which self-aware failure detection may beadded is that the classifier 6 is used to classify the image quality ofthe frame. If the acquisition quality of the frame is classified to bepoor, a segmentation error exception may be thrown (e.g. failure report8 of FIG. 1). If the quality is considered to be good, the problem maybe with the particular segmentation method. Subsequently, anotheralternative segmentation method can be called or manual interaction canbe applied for correction.

The disclosed processing components 4, 6, 8, 12, 14, 18 are of FIG. 1are suitably embodied by an electronic data processing device such as acomputer or parallel computing system. As previously mentioned, themultiple, parallel segmentation processes 12 may advantageously beimplemented using a parallel computing resource such as a multi-corecomputer, supercomputer, computing cluster, GPU, or the like so as toefficiently leverage the parallel processing capability. The classifiertrainer 5 may be implemented using the same computer system as is usedfor processing the input image 2, or may be performed offline by adifferent computer, such as a dedicated server. The manual segmentation10 may be implemented using, for example, a computer providing agraphical user interface (GUI) with a mouse, touchscreen, or other userinput device via which a user may draw contours or otherwise manuallydelineate structure in the input image.

It will be further appreciated that the techniques disclosed herein maybe embodied by a non-transitory storage medium storing instructionsreadable and executable by an electronic data processing device (such asa microprocessor, GPU or so forth) to perform the disclosed techniques.Such a non-transitory storage medium may comprise a hard drive or othermagnetic storage medium, an optical disk or other optical storagemedium, a cloud-based storage medium such as a RAID disk array, flashmemory or other non-volatile electronic storage medium, or so forth.

Of course, modifications and alterations will occur to others uponreading and understanding the preceding description. It is intended thatthe invention be construed as including all such modifications andalterations insofar as they come within the scope of the appended claimsor the equivalents thereof.

The invention claimed is:
 1. An apparatus for segmenting a medicalimage, comprising: a memory that stores instructions; and a processorthat executes the instructions, wherein, when executed by the processor,the instructions cause the processor to: perform multiple, parallelsegmentation processes employing different segmentation processinitializations on an input image to generate a plurality ofsegmentation results, wherein the different segmentation processinitializations are generated by random perturbations of a baselinesegmentation process initialization; mark each segmentation result ofthe multiple, parallel segmentation processes as a potential success(PS) or potential failure (PF); and combine the segmentation resultsmarked as PS to produce an output segmentation result for the inputimage.
 2. The apparatus according to claim 1, wherein: the plurality ofsegmentation results include both intermediate segmentation results anda final segmentation result for each segmentation process of themultiple, parallel segmentation processes; and only the finalsegmentation results marked as PS are combined to produce the outputsegmentation result for the input image.
 3. The apparatus according toclaim 2, wherein the instructions further cause the processor to:declare an overall failure if a percentage of the multiple, parallelsegmentation processes having an intermediate segmentation result markedas PF reaches a predetermined threshold.
 4. The apparatus according toclaim 1, wherein the multiple, parallel segmentation processes areiterative segmentation processes, the plurality of segmentation resultsinclude both intermediate segmentation results produced by non-terminaliterations of the segmentation processes and a final segmentation resultproduced by each segmentation process, and the instructions furthercause the processor to: at each iteration of the iterative segmentationprocesses, adjust a measurement criteria used in marking eachsegmentation result of the multiple, parallel segmentation processes asa PS or PF.
 5. The apparatus according to claim 1, wherein theinstructions cause the processor to mark each segmentation result of themultiple, parallel segmentation processes as a potential success (PS) orpotential failure (PF), the instructions further cause the processor to:identify a largest group of mutually similar segmentation results,wherein: segmentation results belonging to the largest group of mutuallysimilar segmentation results are marked as PS; and segmentation resultsnot belonging to the largest group of mutually similar segmentationresults are marked as PF.
 6. The apparatus according to claim 1, whereinthe instructions further cause the processor to: generate an uncertaintyor confidence interval for the output segmentation result based on astatistical variation of the segmentation results marked as PS.
 7. Theapparatus according to claim 1, wherein each segmentation result ismarked with a probability value P_(PS) of being a PS and with aprobability value P_(PF) of being a PF, where for each segmentationresult P_(PS) is in a range [0,1], P_(PF) is in a range [0,1], andP_(PS)+P_(PF)=1.
 8. The apparatus according to claim 7, wherein prior tothe performing multiple, parallel segmentation processes on the inputimage, the instructions further cause the processor to: preprocess theinput image; and classify, with a binary classifier, the input image asa segmentable or not segmentable, wherein the multiple, parallelsegmentation processes are performed only if the input image isclassified as segmentable.
 9. The apparatus of claim 8, wherein thepreprocessing comprises performing at least one of the following on theinput image: smoothing; contrast enhancement; edge detection; ornon-rigid deformation.
 10. The apparatus according to claim 8, whereinthe instructions further cause the processor to perform a training phasein which the binary classifier is trained by receiving multiple trainingimages wherein each training image of the multiple training images islabeled as segmentable or not segmentable.
 11. A medical system forsegmenting a medical image, the medical system comprising: an input forreceiving an input image; image quality (IQ) binary classifierconfigured to determine in the input image is segmentable; a memory thatstores instructions; and a processor that executes the instructions,wherein, when executed by the processor, the instructions cause theprocessor to: perform multiple, parallel segmentation processesemploying different segmentation process initializations on an inputimage to generate a plurality of segmentation results, wherein thedifferent segmentation process initializations are generated by randomperturbations of a baseline segmentation process initialization; markeach segmentation result of the multiple, parallel segmentationprocesses as a potential success (PS) or potential failure (PF); andcombine the segmentation results marked as PS to produce an outputsegmentation result for the input image.
 12. The medical systemaccording to claim 11, wherein: the plurality of segmentation resultsinclude both intermediate segmentation results and a final segmentationresult for each segmentation process of the multiple, parallelsegmentation processes; and only the final segmentation results markedas PS are combined to produce the output segmentation result for theinput image.
 13. The medical system according to claim 12, wherein theinstructions further cause the processor to: declare an overall failureif a percentage of the multiple, parallel segmentation processes havingan intermediate segmentation result marked as PF reaches a predeterminedthreshold.
 14. The medical system according to claim 11, wherein themultiple, parallel segmentation processes are iterative segmentationprocesses, the plurality of segmentation results include bothintermediate segmentation results produced by non-terminal iterations ofthe segmentation processes and a final segmentation result produced byeach segmentation process, and the instructions further cause theprocessor to: at each iteration of the iterative segmentation processes,adjust a measurement criteria used in marking each segmentation resultof the multiple, parallel segmentation processes as a PS or PF.
 15. Themedical system according to claim 11, wherein the instructions cause theprocessor to mark each segmentation result of the multiple, parallelsegmentation processes as a potential success (PS) or potential failure(PF), the instructions further cause the processor to: identify alargest group of mutually similar segmentation results, wherein:segmentation results belonging to the largest group of mutually similarsegmentation results are marked as PS; and segmentation results notbelonging to the largest group of mutually similar segmentation resultsare marked as PF.
 16. The medical system according to claim 11, whereinthe instructions further cause the processor to: generate an uncertaintyor confidence interval for the output segmentation result based on astatistical variation of the segmentation results marked as PS.
 17. Themedical system according to claim 11, wherein each segmentation resultis marked with a probability value P_(PS) of being a PS and with aprobability value P_(PF) of being a PF, where for each segmentationresult P_(PS) is in a range [0,1], P_(PF) is in a range [0,1], andP_(PS)+P_(PF)=1.
 18. The medical system according to claim 17, whereinprior to the performing multiple, parallel segmentation processes on theinput image, the instructions further cause the processor to: preprocessthe input image; and classify, with a binary classifier, the input imageas a segmentable or not segmentable, wherein the multiple, parallelsegmentation processes are performed only if the input image isclassified as segmentable.
 19. The medical system according to claim 18,wherein the preprocessing comprises performing at least one of thefollowing on the input image: smoothing; contrast enhancement; edgedetection; or non-rigid deformation.
 20. The medical system according toclaim 18, wherein the instructions further cause the processor toperform a training phase in which the binary classifier is trained byreceiving multiple training images wherein each training image of themultiple training images is labeled as segmentable or not segmentable.21. A tangible non-transitory computer readable storage medium thatstores a computer program, the computer program, when executed by aprocessor, causing a medical system to perform a process to segment amedical image, the process comprising: performing multiple, parallelsegmentation processes employing different segmentation processinitializations on an input image to generate a plurality ofsegmentation results, wherein the different segmentation processinitializations are generated by random perturbations of a baselinesegmentation process initialization; marking each segmentation result ofthe multiple, parallel segmentation processes as a potential success(PS) or potential failure (PF); and combining the segmentation resultsmarked as PS to produce an output segmentation result for the inputimage.
 22. The tangible non-transitory computer readable storage mediumaccording to claim 21, wherein the process further comprises: markingeach segmentation result of the multiple, parallel segmentationprocesses as a potential success (PS) or potential failure (PF); andidentifying a largest group of mutually similar segmentation results,wherein: segmentation results belonging to the largest group of mutuallysimilar segmentation results are marked as PS; and segmentation resultsnot belonging to the largest group of mutually similar segmentationresults are marked as PF.